Analysis of selected evolutionary algorithms in feature selection and parameter optimization for data based tumor marker modeling

  • Authors:
  • Stephan M. Winkler;Michael Affenzeller;Gabriel Kronberger;Michael Kommenda;Stefan Wagner;Witold Jacak;Herbert Stekel

  • Affiliations:
  • Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austrian University of Applied Sciences, Hagenberg, Austria;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austrian University of Applied Sciences, Hagenberg, Austria;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austrian University of Applied Sciences, Hagenberg, Austria;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austrian University of Applied Sciences, Hagenberg, Austria;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austrian University of Applied Sciences, Hagenberg, Austria;Heuristic and Evolutionary Algorithms Laboratory School of Informatics, Communications and Media, Upper Austrian University of Applied Sciences, Hagenberg, Austria;Central Laboratory, General Hospital Linz, Linz, Austria

  • Venue:
  • EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
  • Year:
  • 2011

Quantified Score

Hi-index 0.00

Visualization

Abstract

In this paper we report on the use of evolutionary algorithms for optimizing the identification of classification models for selected tumor markers. Our goal is to identify mathematical models that can be used for classifying tumor marker values as normal or as elevated; evolutionary algorithms are used for optimizing the parameters for learning classification models. The sets of variables used as well as the parameter settings for concrete modeling methods are optimized using evolution strategies and genetic algorithms. The performance of these algorithms is analyzed as well as the population diversity progress. In the empirical part of this paper we document modeling results achieved for tumor markers CA 125 and CYFRA using a medical data base provided by the Central Laboratory of the General Hospital Linz; empirical tests are executed using HeuristicLab.